Free viewpoint image generation system using fisheye cameras and a laser rangefinder for indoor robot teleoperation
Free viewpoint image generation system using fisheye cameras and a laser rangefinder for indoor robot teleoperation
复制标题
使用鱼眼相机和激光测距仪的自由视点图像生成系统，用于室内机器人远程操作
DOI：
10.1186/s40648-020-00163-4
复制
发表时间：
2020
期刊：
影响因子：
1.4
通讯作者：
H. Asama
中科院分区：
文献类型：
--
作者：
Ren Komatsu;Hiromitsu Fujii;Y. Tamura;A. Yamashita;H. Asama
在机器人遥操作中，由于缺乏深度信息，机器人经常会与其路径或周围的障碍物发生碰撞。为了解决这个问题，自由视点图像可以极大地帮助操作员避免碰撞，因为操作员能够从任意点的图像中查看机器人的周围环境，从而为他们提供更好的深度信息。本文提出了一种新颖的自由视点图像生成系统。生成自由视点图像的一种方法是使用多个相机和光检测和测距(LiDAR)。这项研究没有使用昂贵的激光雷达，而是使用了经济高效的激光测距仪(LRF)和人造环境的特点。换句话说，我们在机器人上安装了多个鱼眼摄像头和一个LRF。自由视点图像是在墙垂直于地板的假设下生成的。此外，还提出了一种用于估计多个鱼眼摄像机、LRF和机器人模型的姿态的简单校准方法。实验结果表明，该方法可以利用摄像机和LRF生成自由视点图像。最后，利用OpenGL Shading语言对该方法进行了初步实现，利用图形处理单元运算实现了对多幅高分辨率图像的实时处理。补充视频和我们的源代码可在我们的项目页面(https://matsuren.github.io/fvp).
In robot teleoperation, a lack of depth information often results in collisions between the robots and obstacles in its path or surroundings. To address this issue, free viewpoint images can greatly benefit the operators in terms of collision avoidance as the operators are able to view the robot’s surrounding from the images at arbitrary points, giving them a better depth information. In this paper, a novel free viewpoint image generation system is proposed. One approach to generate free viewpoint images is to use multiple cameras and Light Detection and Ranging (LiDAR). Instead of using the expensive LiDAR, this study utilizes a cost-effective laser rangefinder (LRF) and a characteristic of man-made environments. In other words, we install multiple fisheye cameras and an LRF on a robot. Free viewpoint images are generated under the assumption that walls are perpendicular to the floor. Furthermore, an easy calibration for estimating the poses of the multiple fisheye cameras, the LRF, and the robot model is proposed. Experimental results show that the proposed method can generate free viewpoint images using cameras and an LRF. Finally, the proposed method is primarily implemented using OpenGL Shading Language to utilize a graphics processing unit computation to achieve a real-time processing of the multiple high-resolution images. Supplementary videos and our source code are available at our project page (https://matsuren.github.io/fvp).